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. 2022 Sep 16;12(1):15622.
doi: 10.1038/s41598-022-19817-x.

A machine learning COVID-19 mass screening based on symptoms and a simple olfactory test

Youcef Azeli  1   2   3 , Alberto Fernández  4 , Federico Capriles  5 , Wojciech Rojewski  5 , Vanesa Lopez-Madrid  5 , David Sabaté-Lissner  6 , Rosa Maria Serrano  5   7 , Cristina Rey-Reñones  8   9   10 , Marta Civit  6 , Josefina Casellas  5 , Abdelghani El Ouahabi-El Ouahabi  5 , Maria Foglia-Fernández  11 , Salvador Sarrá  12 , Eduard Llobet  13
Affiliations

A machine learning COVID-19 mass screening based on symptoms and a simple olfactory test

Youcef Azeli et al. Sci Rep. .

Abstract

The early detection of symptoms and rapid testing are the basis of an efficient screening strategy to control COVID-19 transmission. The olfactory dysfunction is one of the most prevalent symptom and in many cases is the first symptom. This study aims to develop a machine learning COVID-19 predictive tool based on symptoms and a simple olfactory test, which consists of identifying the smell of an aromatized hydroalcoholic gel. A multi-centre population-based prospective study was carried out in the city of Reus (Catalonia, Spain). The study included consecutive patients undergoing a reverse transcriptase polymerase chain reaction test for presenting symptoms suggestive of COVID-19 or for being close contacts of a confirmed COVID-19 case. A total of 519 patients were included, 386 (74.4%) had at least one symptom and 133 (25.6%) were asymptomatic. A classification tree model including sex, age, relevant symptoms and the olfactory test results obtained a sensitivity of 0.97 (95% CI 0.91-0.99), a specificity of 0.39 (95% CI 0.34-0.44) and an AUC of 0.87 (95% CI 0.83-0.92). This shows that this machine learning predictive model is a promising mass screening for COVID-19.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Flow chart.
Figure 2
Figure 2
Sensitive and specific classification tree algorithm ROC curve.
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